Overview

Dataset statistics

Number of variables16
Number of observations29748
Missing cells2912
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.4 MiB
Average record size in memory366.0 B

Variable types

Numeric10
Categorical6

Alerts

Location has constant value "172.30.24.203" Constant
MessageTime has a high cardinality: 29748 distinct values High cardinality
AmbientTemperature is highly correlated with AmbientTemperatureHigh and 4 other fieldsHigh correlation
AmbientTemperatureHigh is highly correlated with AmbientTemperature and 3 other fieldsHigh correlation
AmbientTemperatureLow is highly correlated with AmbientTemperature and 3 other fieldsHigh correlation
HeatIndex is highly correlated with AmbientTemperature and 4 other fieldsHigh correlation
DewPoint is highly correlated with AmbientTemperature and 3 other fieldsHigh correlation
RelativeHumidity is highly correlated with AmbientTemperature and 1 other fieldsHigh correlation
BarometricPressure is highly correlated with BarometricPressureN3High correlation
BarometricPressureN3 is highly correlated with BarometricPressureHigh correlation
AmbientTemperature is highly correlated with AmbientTemperatureHigh and 3 other fieldsHigh correlation
AmbientTemperatureHigh is highly correlated with AmbientTemperature and 3 other fieldsHigh correlation
AmbientTemperatureLow is highly correlated with AmbientTemperature and 3 other fieldsHigh correlation
HeatIndex is highly correlated with AmbientTemperature and 3 other fieldsHigh correlation
DewPoint is highly correlated with AmbientTemperature and 3 other fieldsHigh correlation
AmbientTemperature is highly correlated with AmbientTemperatureHigh and 3 other fieldsHigh correlation
AmbientTemperatureHigh is highly correlated with AmbientTemperature and 3 other fieldsHigh correlation
AmbientTemperatureLow is highly correlated with AmbientTemperature and 3 other fieldsHigh correlation
HeatIndex is highly correlated with AmbientTemperature and 3 other fieldsHigh correlation
DewPoint is highly correlated with AmbientTemperature and 3 other fieldsHigh correlation
BarometricPressure is highly correlated with BarometricPressureN3High correlation
BarometricPressureN3 is highly correlated with BarometricPressureHigh correlation
WindDirection is highly correlated with LocationHigh correlation
BarometricPressureStatus is highly correlated with LocationHigh correlation
Location is highly correlated with WindDirection and 3 other fieldsHigh correlation
RainToday is highly correlated with LocationHigh correlation
RainLastHour is highly correlated with LocationHigh correlation
Id is highly correlated with AmbientTemperature and 11 other fieldsHigh correlation
AmbientTemperature is highly correlated with Id and 7 other fieldsHigh correlation
AmbientTemperatureHigh is highly correlated with Id and 7 other fieldsHigh correlation
AmbientTemperatureLow is highly correlated with Id and 10 other fieldsHigh correlation
HeatIndex is highly correlated with Id and 7 other fieldsHigh correlation
DewPoint is highly correlated with Id and 7 other fieldsHigh correlation
RelativeHumidity is highly correlated with Id and 8 other fieldsHigh correlation
BarometricPressure is highly correlated with Id and 7 other fieldsHigh correlation
BarometricPressureN3 is highly correlated with Id and 2 other fieldsHigh correlation
BarometricPressureStatus is highly correlated with Id and 3 other fieldsHigh correlation
WindDirection is highly correlated with Id and 7 other fieldsHigh correlation
RainToday is highly correlated with Id and 4 other fieldsHigh correlation
RainLastHour is highly correlated with Id and 1 other fieldsHigh correlation
WindDirection has 2912 (9.8%) missing values Missing
MessageTime is uniformly distributed Uniform
Id has unique values Unique
MessageTime has unique values Unique
BarometricPressureN3 has 836 (2.8%) zeros Zeros
WindSpeed has 1488 (5.0%) zeros Zeros

Reproduction

Analysis started2022-05-02 12:36:27.192757
Analysis finished2022-05-02 12:36:41.489766
Duration14.3 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct29748
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1626583990
Minimum1621346390
Maximum1637096286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.5 KiB
2022-05-02T07:36:41.589470image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1621346390
5-th percentile1621462314
Q11622311850
median1625324057
Q31631384888
95-th percentile1632667506
Maximum1637096286
Range15749896
Interquartile range (IQR)9073039

Descriptive statistics

Standard deviation4384437.989
Coefficient of variation (CV)0.002695488224
Kurtosis-1.269404601
Mean1626583990
Median Absolute Deviation (MAD)3578058
Skewness0.3575351747
Sum4.838762052 × 1013
Variance1.922329648 × 1013
MonotonicityStrictly increasing
2022-05-02T07:36:41.716188image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16213463901
 
< 0.1%
16294789201
 
< 0.1%
16294792801
 
< 0.1%
16294792501
 
< 0.1%
16294792201
 
< 0.1%
16294791901
 
< 0.1%
16294791601
 
< 0.1%
16294791301
 
< 0.1%
16294791001
 
< 0.1%
16294790701
 
< 0.1%
Other values (29738)29738
> 99.9%
ValueCountFrequency (%)
16213463901
< 0.1%
16213464201
< 0.1%
16213464501
< 0.1%
16213464801
< 0.1%
16213468101
< 0.1%
16213468401
< 0.1%
16213476201
< 0.1%
16213476501
< 0.1%
16213477701
< 0.1%
16213478001
< 0.1%
ValueCountFrequency (%)
16370962861
< 0.1%
16370962561
< 0.1%
16370962261
< 0.1%
16370961961
< 0.1%
16370961661
< 0.1%
16370961361
< 0.1%
16370961061
< 0.1%
16370960761
< 0.1%
16370960461
< 0.1%
16370960161
< 0.1%

MessageTime
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct29748
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2021-05-18 13:59:50.000
 
1
2021-08-20 13:02:00.000
 
1
2021-08-20 13:08:00.000
 
1
2021-08-20 13:07:30.000
 
1
2021-08-20 13:07:00.000
 
1
Other values (29743)
29743 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29748 ?
Unique (%)100.0%

Sample

1st row2021-05-18 13:59:50.000
2nd row2021-05-18 14:00:20.000
3rd row2021-05-18 14:00:50.000
4th row2021-05-18 14:01:20.000
5th row2021-05-18 14:06:50.000

Common Values

ValueCountFrequency (%)
2021-05-18 13:59:50.0001
 
< 0.1%
2021-08-20 13:02:00.0001
 
< 0.1%
2021-08-20 13:08:00.0001
 
< 0.1%
2021-08-20 13:07:30.0001
 
< 0.1%
2021-08-20 13:07:00.0001
 
< 0.1%
2021-08-20 13:06:30.0001
 
< 0.1%
2021-08-20 13:06:00.0001
 
< 0.1%
2021-08-20 13:05:30.0001
 
< 0.1%
2021-08-20 13:05:00.0001
 
< 0.1%
2021-08-20 13:04:30.0001
 
< 0.1%
Other values (29738)29738
> 99.9%

Length

2022-05-02T07:36:41.835169image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-05-292879
 
4.8%
2021-05-302191
 
3.7%
2021-09-111389
 
2.3%
2021-08-211277
 
2.1%
2021-09-251223
 
2.1%
2021-08-081123
 
1.9%
2021-07-031099
 
1.8%
2021-09-261022
 
1.7%
2021-09-181017
 
1.7%
2021-09-19983
 
1.7%
Other values (21521)45293
76.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AmbientTemperature
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.08847654
Minimum0
Maximum92
Zeros39
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size232.5 KiB
2022-05-02T07:36:41.934878image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49
Q165
median73
Q382
95-th percentile89
Maximum92
Range92
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.14163112
Coefficient of variation (CV)0.1684267959
Kurtosis0.6637903233
Mean72.08847654
Median Absolute Deviation (MAD)9
Skewness-0.6365920525
Sum2144488
Variance147.4192063
MonotonicityNot monotonic
2022-05-02T07:36:42.052355image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
681356
 
4.6%
861346
 
4.5%
821175
 
3.9%
721148
 
3.9%
741135
 
3.8%
481104
 
3.7%
661078
 
3.6%
751051
 
3.5%
811028
 
3.5%
88924
 
3.1%
Other values (38)18403
61.9%
ValueCountFrequency (%)
039
 
0.1%
4619
 
0.1%
47208
 
0.7%
481104
3.7%
49375
 
1.3%
50237
 
0.8%
51321
 
1.1%
52247
 
0.8%
53184
 
0.6%
54798
2.7%
ValueCountFrequency (%)
9210
 
< 0.1%
91185
 
0.6%
90556
1.9%
89802
2.7%
88924
3.1%
87920
3.1%
861346
4.5%
85769
2.6%
84920
3.1%
83817
2.7%

AmbientTemperatureHigh
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.07802205
Minimum54
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.5 KiB
2022-05-02T07:36:42.166372image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile54
Q169
median77
Q384
95-th percentile89
Maximum92
Range38
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.09496028
Coefficient of variation (CV)0.1326922021
Kurtosis-0.540652886
Mean76.07802205
Median Absolute Deviation (MAD)7
Skewness-0.5855149204
Sum2263169
Variance101.908223
MonotonicityNot monotonic
2022-05-02T07:36:42.281802image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
762138
 
7.2%
832071
 
7.0%
631947
 
6.5%
541892
 
6.4%
771685
 
5.7%
841413
 
4.7%
821309
 
4.4%
861287
 
4.3%
891200
 
4.0%
851174
 
3.9%
Other values (29)13632
45.8%
ValueCountFrequency (%)
541892
6.4%
5514
 
< 0.1%
5656
 
0.2%
5718
 
0.1%
58105
 
0.4%
5954
 
0.2%
60284
 
1.0%
61247
 
0.8%
62101
 
0.3%
631947
6.5%
ValueCountFrequency (%)
92144
 
0.5%
91625
 
2.1%
90552
 
1.9%
891200
4.0%
88923
3.1%
871046
3.5%
861287
4.3%
851174
3.9%
841413
4.7%
832071
7.0%

AmbientTemperatureLow
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.10286406
Minimum46
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.5 KiB
2022-05-02T07:36:42.405699image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile48
Q156
median64
Q368
95-th percentile83
Maximum91
Range45
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.842674827
Coefficient of variation (CV)0.1559782583
Kurtosis-0.1570455333
Mean63.10286406
Median Absolute Deviation (MAD)7
Skewness0.2999384151
Sum1877184
Variance96.87824776
MonotonicityNot monotonic
2022-05-02T07:36:42.519109image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
652837
 
9.5%
482438
 
8.2%
661998
 
6.7%
541866
 
6.3%
591729
 
5.8%
671559
 
5.2%
711378
 
4.6%
611365
 
4.6%
741359
 
4.6%
571227
 
4.1%
Other values (35)11992
40.3%
ValueCountFrequency (%)
461016
3.4%
4761
 
0.2%
482438
8.2%
49234
 
0.8%
50189
 
0.6%
51243
 
0.8%
52112
 
0.4%
53427
 
1.4%
541866
6.3%
5543
 
0.1%
ValueCountFrequency (%)
9119
 
0.1%
90235
 
0.8%
8954
 
0.2%
8884
 
0.3%
8711
 
< 0.1%
8638
 
0.1%
8597
 
0.3%
84907
3.0%
83189
 
0.6%
8294
 
0.3%

HeatIndex
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.17591099
Minimum0
Maximum101
Zeros39
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size232.5 KiB
2022-05-02T07:36:42.640270image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49
Q165
median73
Q383
95-th percentile95
Maximum101
Range101
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.59719831
Coefficient of variation (CV)0.1858152243
Kurtosis0.1932860358
Mean73.17591099
Median Absolute Deviation (MAD)10
Skewness-0.263654386
Sum2176837
Variance184.883802
MonotonicityNot monotonic
2022-05-02T07:36:42.767293image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
681356
 
4.6%
821223
 
4.1%
861158
 
3.9%
721148
 
3.9%
741135
 
3.8%
481104
 
3.7%
661078
 
3.6%
751051
 
3.5%
71843
 
2.8%
94809
 
2.7%
Other values (47)18843
63.3%
ValueCountFrequency (%)
039
 
0.1%
4619
 
0.1%
47208
 
0.7%
481104
3.7%
49375
 
1.3%
50237
 
0.8%
51321
 
1.1%
52247
 
0.8%
53184
 
0.6%
54798
2.7%
ValueCountFrequency (%)
1015
 
< 0.1%
10068
 
0.2%
99168
 
0.6%
98272
 
0.9%
97214
 
0.7%
96446
1.5%
95563
1.9%
94809
2.7%
93539
1.8%
92315
 
1.1%

DewPoint
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.86620949
Minimum0
Maximum76
Zeros39
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size232.5 KiB
2022-05-02T07:36:42.887096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40
Q153
median59
Q363
95-th percentile73
Maximum76
Range76
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.91555008
Coefficient of variation (CV)0.1713530257
Kurtosis0.8510146186
Mean57.86620949
Median Absolute Deviation (MAD)5
Skewness-0.6565093116
Sum1721404
Variance98.31813338
MonotonicityNot monotonic
2022-05-02T07:36:42.997861image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
612465
 
8.3%
622422
 
8.1%
582237
 
7.5%
601987
 
6.7%
571511
 
5.1%
401386
 
4.7%
591274
 
4.3%
561162
 
3.9%
731035
 
3.5%
701026
 
3.4%
Other values (32)13243
44.5%
ValueCountFrequency (%)
039
 
0.1%
36117
 
0.4%
37291
 
1.0%
38457
 
1.5%
39268
 
0.9%
401386
4.7%
41487
 
1.6%
42258
 
0.9%
43442
 
1.5%
44804
2.7%
ValueCountFrequency (%)
7666
 
0.2%
75147
 
0.5%
74621
2.1%
731035
3.5%
72768
2.6%
71528
1.8%
701026
3.4%
69766
2.6%
68282
 
0.9%
67473
1.6%

RelativeHumidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct64
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.39000941
Minimum0
Maximum95
Zeros39
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size232.5 KiB
2022-05-02T07:36:43.119627image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37
Q151
median63
Q373
95-th percentile86
Maximum95
Range95
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.04535686
Coefficient of variation (CV)0.2411500976
Kurtosis-0.5521710858
Mean62.39000941
Median Absolute Deviation (MAD)11
Skewness-0.1458895912
Sum1855978
Variance226.362763
MonotonicityNot monotonic
2022-05-02T07:36:43.362157image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67962
 
3.2%
62906
 
3.0%
66858
 
2.9%
68832
 
2.8%
45826
 
2.8%
63789
 
2.7%
61781
 
2.6%
69722
 
2.4%
75720
 
2.4%
70712
 
2.4%
Other values (54)21640
72.7%
ValueCountFrequency (%)
039
 
0.1%
3333
 
0.1%
34501
1.7%
35335
1.1%
36367
1.2%
37415
1.4%
38380
1.3%
39343
1.2%
40276
0.9%
41346
1.2%
ValueCountFrequency (%)
9519
 
0.1%
94148
0.5%
9321
 
0.1%
9222
 
0.1%
91125
 
0.4%
90201
0.7%
89205
0.7%
88299
1.0%
87350
1.2%
86300
1.0%

BarometricPressure
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct115
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.42184214
Minimum28.54
Maximum30.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size232.5 KiB
2022-05-02T07:36:43.486933image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum28.54
5-th percentile28.6
Q129.27
median29.45
Q329.6
95-th percentile30.18
Maximum30.27
Range1.73
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.4310390118
Coefficient of variation (CV)0.01465030673
Kurtosis-0.1604534351
Mean29.42184214
Median Absolute Deviation (MAD)0.17
Skewness-0.1804628058
Sum875240.96
Variance0.1857946297
MonotonicityNot monotonic
2022-05-02T07:36:43.613119image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.45978
 
3.3%
29.46943
 
3.2%
29.5851
 
2.9%
29.32832
 
2.8%
29.49746
 
2.5%
29.31736
 
2.5%
29.33730
 
2.5%
29.36707
 
2.4%
29.51620
 
2.1%
29.48604
 
2.0%
Other values (105)22001
74.0%
ValueCountFrequency (%)
28.5417
 
0.1%
28.5599
 
0.3%
28.56112
 
0.4%
28.57258
0.9%
28.58311
1.0%
28.59421
1.4%
28.6289
1.0%
28.61130
 
0.4%
28.62198
0.7%
28.63313
1.1%
ValueCountFrequency (%)
30.2716
 
0.1%
30.26195
0.7%
30.25274
0.9%
30.2498
 
0.3%
30.23119
 
0.4%
30.2273
 
0.2%
30.21107
 
0.4%
30.263
 
0.2%
30.19164
 
0.6%
30.18420
1.4%

BarometricPressureN3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct99
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.5990117
Minimum0
Maximum30.26
Zeros836
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size232.5 KiB
2022-05-02T07:36:43.742790image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.59
Q129.25
median29.44
Q329.64
95-th percentile30.18
Maximum30.26
Range30.26
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation4.88219462
Coefficient of variation (CV)0.1707120047
Kurtosis30.09937398
Mean28.5990117
Median Absolute Deviation (MAD)0.19
Skewness-5.64081227
Sum850763.4
Variance23.8358243
MonotonicityNot monotonic
2022-05-02T07:36:43.863904image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.311080
 
3.6%
29.661044
 
3.5%
29.46929
 
3.1%
0836
 
2.8%
29.36828
 
2.8%
29.51778
 
2.6%
29.44773
 
2.6%
29.32741
 
2.5%
29.35717
 
2.4%
29.45657
 
2.2%
Other values (89)21365
71.8%
ValueCountFrequency (%)
0836
2.8%
28.51120
 
0.4%
28.53120
 
0.4%
28.55120
 
0.4%
28.57240
 
0.8%
28.59140
 
0.5%
28.6120
 
0.4%
28.61353
1.2%
28.62273
 
0.9%
28.63240
 
0.8%
ValueCountFrequency (%)
30.26240
0.8%
30.25319
1.1%
30.24120
 
0.4%
30.23356
1.2%
30.21120
 
0.4%
30.2120
 
0.4%
30.18255
0.9%
30.17120
 
0.4%
30.16120
 
0.4%
30.15120
 
0.4%

BarometricPressureStatus
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Steady
23978 
Falling
3490 
Rising
 
1444
RisingRapidly
 
836

Length

Max length13
Median length6
Mean length6.314037919
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSteady
2nd rowSteady
3rd rowSteady
4th rowSteady
5th rowSteady

Common Values

ValueCountFrequency (%)
Steady23978
80.6%
Falling3490
 
11.7%
Rising1444
 
4.9%
RisingRapidly836
 
2.8%

Length

2022-05-02T07:36:43.980906image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-02T07:36:44.048939image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
steady23978
80.6%
falling3490
 
11.7%
rising1444
 
4.9%
risingrapidly836
 
2.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WindSpeed
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.390480032
Minimum0
Maximum17
Zeros1488
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size232.5 KiB
2022-05-02T07:36:44.119051image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.35
Q12
median4
Q36
95-th percentile9
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.580640029
Coefficient of variation (CV)0.5877808372
Kurtosis-0.1938948967
Mean4.390480032
Median Absolute Deviation (MAD)2
Skewness0.4382052675
Sum130608
Variance6.65970296
MonotonicityNot monotonic
2022-05-02T07:36:44.211073image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
34255
14.3%
44239
14.2%
24179
14.0%
53757
12.6%
63278
11.0%
72599
8.7%
12192
7.4%
81735
5.8%
01488
 
5.0%
91091
 
3.7%
Other values (7)935
 
3.1%
ValueCountFrequency (%)
01488
 
5.0%
12192
7.4%
24179
14.0%
34255
14.3%
44239
14.2%
53757
12.6%
63278
11.0%
72599
8.7%
81735
5.8%
91091
 
3.7%
ValueCountFrequency (%)
172
 
< 0.1%
155
 
< 0.1%
1417
 
0.1%
1342
 
0.1%
1297
 
0.3%
11215
 
0.7%
10557
 
1.9%
91091
3.7%
81735
5.8%
72599
8.7%

WindDirection
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)0.1%
Missing2912
Missing (%)9.8%
Memory size1.6 MiB
NNW
5591 
SSE
2771 
WNW
2739 
NW
2390 
S
2251 
Other values (11)
11094 

Length

Max length3
Median length3
Mean length2.380943509
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSSE
2nd rowSE
3rd rowSSE
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
NNW5591
18.8%
SSE2771
9.3%
WNW2739
9.2%
NW2390
8.0%
S2251
7.6%
W1858
 
6.2%
SSW1430
 
4.8%
SW1203
 
4.0%
NNE1083
 
3.6%
WSW1011
 
3.4%
Other values (6)4509
15.2%
(Missing)2912
9.8%

Length

2022-05-02T07:36:44.323868image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nnw5591
20.8%
sse2771
10.3%
wnw2739
10.2%
nw2390
8.9%
s2251
8.4%
w1858
 
6.9%
ssw1430
 
5.3%
sw1203
 
4.5%
nne1083
 
4.0%
wsw1011
 
3.8%
Other values (6)4509
16.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RainToday
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
29210 
0.01
 
538

Length

Max length4
Median length3
Mean length3.018085249
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.029210
98.2%
0.01538
 
1.8%

Length

2022-05-02T07:36:44.424045image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-02T07:36:44.484377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.029210
98.2%
0.01538
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RainLastHour
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
29670 
0.01
 
78

Length

Max length4
Median length3
Mean length3.002622025
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.029670
99.7%
0.0178
 
0.3%

Length

2022-05-02T07:36:44.549531image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-02T07:36:44.609622image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.029670
99.7%
0.0178
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Location
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
172.30.24.203
29748 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row172.30.24.203
2nd row172.30.24.203
3rd row172.30.24.203
4th row172.30.24.203
5th row172.30.24.203

Common Values

ValueCountFrequency (%)
172.30.24.20329748
100.0%

Length

2022-05-02T07:36:44.671098image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-02T07:36:44.727425image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
172.30.24.20329748
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-05-02T07:36:39.466684image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:28.687569image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:29.946393image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:31.041140image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:32.339330image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:33.455216image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:34.569947image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:35.878889image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:36.983182image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:38.312078image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:39.596595image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:28.813143image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:30.061313image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:31.162414image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:32.465755image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:33.578018image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:34.691880image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:35.993831image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:37.109953image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:38.432435image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:39.707814image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:28.920500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:30.159889image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:31.276693image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:32.575286image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:33.678367image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:34.801828image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:36.095927image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:37.222857image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:38.547214image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:39.828000image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:29.037791image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:30.269014image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:31.394637image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:32.687434image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:33.792043image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:34.922661image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:36.211079image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:37.347583image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:38.665893image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:39.940492image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:29.147247image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:30.368914image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:31.516310image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:32.791179image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:33.896021image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:35.034808image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:36.315772image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:37.462916image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:38.779929image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:40.053207image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:29.259376image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:30.476613image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:31.627646image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:32.896208image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:33.999170image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:35.278639image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:36.421888image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:37.585841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:38.886178image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:40.173662image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:29.471971image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:30.594849image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:31.751985image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:33.007298image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:34.110532image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:35.409714image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:36.541381image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:37.707002image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:39.005019image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:40.283972image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:29.588208image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:30.701505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:31.863069image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:33.110098image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:34.212566image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:35.523880image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:36.642032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:37.818422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:39.112376image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:40.405917image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:29.710036image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:30.817340image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:31.984446image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:33.227308image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:34.330818image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:35.648247image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:36.759381image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:37.945320image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:39.233231image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:40.525581image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:29.826606image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:30.926779image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:32.101931image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:33.341096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:34.446583image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:35.763268image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:36.869492image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:38.181793image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-05-02T07:36:39.349680image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2022-05-02T07:36:44.792914image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-02T07:36:44.978804image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-02T07:36:45.164427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-02T07:36:45.466730image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-02T07:36:45.617273image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-02T07:36:40.775862image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-02T07:36:41.175401image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-02T07:36:41.367524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IdMessageTimeAmbientTemperatureAmbientTemperatureHighAmbientTemperatureLowHeatIndexDewPointRelativeHumidityBarometricPressureBarometricPressureN3BarometricPressureStatusWindSpeedWindDirectionRainTodayRainLastHourLocation
016213463902021-05-18 13:59:50.00066666166597829.5029.46Steady3SSE0.00.0172.30.24.203
116213464202021-05-18 14:00:20.00066666166597829.5029.47Steady3SE0.00.0172.30.24.203
216213464502021-05-18 14:00:50.00066666166597829.5029.47Steady3SSE0.00.0172.30.24.203
316213464802021-05-18 14:01:20.00066666166597829.5029.47Steady4S0.00.0172.30.24.203
416213468102021-05-18 14:06:50.00066666166607829.5029.47Steady4S0.00.0172.30.24.203
516213468402021-05-18 14:07:20.00067676167597729.5029.47Steady5S0.00.0172.30.24.203
616213476202021-05-18 14:20:20.00068686168607629.4929.47Steady5S0.00.0172.30.24.203
716213476502021-05-18 14:20:50.00068686168607629.5029.47Steady5SSE0.00.0172.30.24.203
816213477702021-05-18 14:22:50.00068686168607629.5029.47Steady4SSE0.00.0172.30.24.203
916213478002021-05-18 14:23:20.00068686168607629.5029.47Steady5SSE0.00.0172.30.24.203

Last rows

IdMessageTimeAmbientTemperatureAmbientTemperatureHighAmbientTemperatureLowHeatIndexDewPointRelativeHumidityBarometricPressureBarometricPressureN3BarometricPressureStatusWindSpeedWindDirectionRainTodayRainLastHourLocation
2973816370960162021-11-16 15:53:36.00075757175453429.3129.35Falling0NNW0.010.0172.30.24.203
2973916370960462021-11-16 15:54:06.00075757175453429.3129.35Falling0NNW0.010.0172.30.24.203
2974016370960762021-11-16 15:54:36.00075757175453429.3229.35Steady0NNW0.010.0172.30.24.203
2974116370961062021-11-16 15:55:06.00075757175453329.3129.35Falling0NNW0.010.0172.30.24.203
2974216370961362021-11-16 15:55:36.00075757175453429.3129.35Falling0NNW0.010.0172.30.24.203
2974316370961662021-11-16 15:56:06.00075757175453429.3129.35Falling0NNW0.010.0172.30.24.203
2974416370961962021-11-16 15:56:36.00075757175453429.3229.35Steady0NNW0.010.0172.30.24.203
2974516370962262021-11-16 15:57:06.00075757175453429.3129.35Falling0NNW0.010.0172.30.24.203
2974616370962562021-11-16 15:57:36.00075757175453429.3129.35Falling0NNW0.010.0172.30.24.203
2974716370962862021-11-16 15:58:06.00075757175453429.3129.35Falling0NNW0.010.0172.30.24.203